This course will cover the Measure phase and portions of the Analyze phase of the Six Sigma DMAIC (Define, Measure, Analyze, Improve, and Control) process. You will learn about lean tools for process analysis, failure mode and effects analysis (FMEA), measurement system analysis (MSA) and gauge repeatability and reproducibility (GR&R), and you will be introduced to basic statistics. This course will outline useful measure and analysis phase tools and will give you an overview of statistics as they are related to the Six Sigma process.
The statistics module will provide you with an overview of the concepts and you will be given multiple example problems to see how to apply these concepts. Every module will include readings, discussions, lecture videos, and quizzes to help make sure you understand the material and concepts that are studied.
Our applied curriculum is built around the latest handbook The Certified Six Sigma Handbook (2nd edition) and students will develop /learn the fundamentals of Six Sigma. Registration includes online access to course content, projects, and resources but does not include the companion text The Certified Six Sigma Handbook (2nd edition). The companion text is not required to complete the assignments. However, the text is a recognized handbook used by professionals in the field. Also, it is a highly recommended text for those wishing to move forward in Six Sigma and eventually gain certification from professional agencies such as American Society for Quality (ASQ).

SS

I am always afraid of the normal distribution. But in the professor's way in this course made me easily understand about it.

從本節課中

Data Analysis

In this module you will be diving into the statistical side of Six Sigma. You will begin with learning about the basic distribution types which include normal and binomial. You will then proceed to variation and will learn the difference between common and special cause variation.

David Cook, PhD

Gregory Wiles, PhD

Bill Bailey, PhD

腳本

A Probability Distribution is a mathematical function that provides the probability of occurences at different possible outcomes in an experiment. For a discrete distribution, this can be visually represented with a histogram and the probability of the random value having outcome X, would be the height of the histogram at X. A discreet example is the probability distribution for tossing a die. This particular example is pretty boring because the probability of each discreet outcome X from 1 to 6 would be one sixth. The probability of rolling and the other value such as rolling a zero or rolling a nine is zero, so it's typically not pictured in the distribution. Continuous Distributions are represented visually as a curve. A continuous example is the distribution of heights of women in the United States. The probability of the continuous variable having an outcome between X1 and X2 would be the area under the curve between the points X1 and X2. So we could calculate the probability that, if we randomly select one female from the United States she would be between 60 and 65 inches tall by determining the area under the curve between 60 and 65. A distribution is symmetric if the mean is equal to the median and the two sides on the mean are mere images. The normal distribution is an example of a symmetric continuous distribution and is in the shape of a bell curve. The height example we just looked was also symmetric. When the curve is asymmetric, with a tail off to the left, the mean is less than the median and the distribution is called Left Skewed. When the curve is asymmetric with a tail off to the right, the mean is greater than the median and the distribution is called Right Skewed. Distributions can also have more than one peak, or more than one mode. A distribution with two modes is called bimodal. A distribution with more than one mode in general is called, multimodal.